Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

A computational approach to distinguish somatic vs. germline origin of genomic alterations from deep sequencing of cancer specimens without a matched normal.

PLoS computational biology - 1 Feb 2018

Sun James X, He Yuting, Sanford Eric, Montesion Meagan, Frampton Garrett M, Vignot Stéphane, Soria Jean-Charles, Ross Jeffrey S, Miller Vincent A, Stephens Phil J, Lipson Doron, Yelensky Roman

Abstract excerpt

A key constraint in genomic testing in oncology is that matched normal specimens are not commonly obtained in clinical practice. Thus, while well-characterized genomic alterations do not require normal tissue for interpretation, a significant number of alterations will be unknown in whether they are germline or somatic, in the absence of a matched normal control. We introduce SGZ (somatic-germline-zygosity), a...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Carcinoma, Non-Small-Cell Lung
Share to Colonic Neoplasms

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Databases, Genetic

Share to DNA Copy Number Variations

Share to Germ-Line Mutation

Share to Ploidies

Share to Probability

Source

Open in PubMed Open full text

Identifiers

PMID: 29415044
DOI: 10.1371/journal.pcbi.1005965
PMCID: PMC5832436

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 3 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

A computational approach to distinguish somatic vs. germline origin of genomic alterations from deep sequencing of cancer specimens without a matched normal.PMID 29415044Read ↗

SomaticSniper: identification of somatic point mutations in whole genome sequencing data.Semantic proximity Read ↗

Somatically Mutated Genes Under Positive and Negative Selection Found by Transcriptome Sequence Analysis Include Oncogene and Tumor Suppressor CandidatesSemantic proximity Read ↗

SomatiCA: identifying, characterizing and quantifying somatic copy number aberrations from cancer genome sequencing data.Semantic proximity Read ↗

Accurate <i>de novo</i> detection of somatic mutations in high-throughput single-cell profiling data setsSemantic proximity Read ↗

Landscape of human protein-coding somatic mutations across tissues and individualsSemantic proximity Read ↗

Accurate de novo detection of somatic mutations in high-throughput single-cell profiling data setsSemantic proximity Read ↗

ISOWN: accurate somatic mutation identification in the absence of normal tissue controls.Semantic proximity Read ↗

Shimmer: detection of genetic alterations in tumors using next-generation sequence data.Semantic proximity Read ↗

Somatic variant detection in normal tissues from single-cell sequencing dataSemantic proximity Read ↗

De novo detection of somatic mutations in high-throughput single-cell profiling data sets.Semantic proximity Read ↗

Precise identification of somatic and germline variants in the absence of matched normal samples.Semantic proximity Read ↗

Joint analysis of matched tumor samples with varying tumor contents improves somatic variant calling in the absence of a germline sampleSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.